REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization
It addresses scalability and generalization challenges in multiplex influence maximization for social online platforms, representing an incremental improvement over existing learning-based approaches.
The paper tackles the problem of identifying influential seed users in multiplex social networks to maximize influence spread, proposing the Reinforced Expert Maximization (REM) framework, which outperforms state-of-the-art methods in influence spread, scalability, and inference time on real-world datasets.
In social online platforms, identifying influential seed users to maximize influence spread is a crucial as it can greatly diminish the cost and efforts required for information dissemination. While effective, traditional methods for Multiplex Influence Maximization (MIM) have reached their performance limits, prompting the emergence of learning-based approaches. These novel methods aim for better generalization and scalability for more sizable graphs but face significant challenges, such as (1) inability to handle unknown diffusion patterns and (2) reliance on high-quality training samples. To address these issues, we propose the Reinforced Expert Maximization framework (REM). REM leverages a Propagation Mixture of Experts technique to encode dynamic propagation of large multiplex networks effectively in order to generate enhanced influence propagation. Noticeably, REM treats a generative model as a policy to autonomously generate different seed sets and learn how to improve them from a Reinforcement Learning perspective. Extensive experiments on several real-world datasets demonstrate that REM surpasses state-of-the-art methods in terms of influence spread, scalability, and inference time in influence maximization tasks.